Bioimage analysis is a multidisciplinary scientific discipline that draws on mathematics, signal processing and physics, to extract exhaustive information from image data through computer programs. Accordingly, research at the BioImage Analysis Unit is dedicated to design innovative computational methods to address challenging biological questions through rigorous quantitative approaches.
Our research projects encompass the topics of machine learning, mathematical imaging, statistical analysis, shape classification, and computational cell biophysics. They address the spatial analysis of biomolecules, the dynamics of sub-cellular organelles, the mechanobiology of cell motility, the spatio-temporal orchestration of cellular trafficking, the spreading of pathogens, the analysis of social behaviour in mice, and digital pathology. Our methods range from biophysically augmented optical flow, active contours models, to spatial statistics and deep learning.
We actively develop and maintain the free and open source image analysis software Icy (http://icy.bioimageanalysis.org), which will soon include deepIcy, a dedicated plugin to run DL models directly from within the application.
